Gene and pathway-based second-wave analysis of genome-wide association studies

Abstract

Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.

Figure 1

P-values of genes in GnRH pathway for RA. (a) P-values of genes in GnRH pathway for RA in WTCCC studies. Blocks containing significant genes are in red color, blocks containing mild significant genes are in light red color and blocks containing no significant genes are in green color. (b) P-values of genes in GnRH pathway for RA in NARAC and EIRA studies. Blocks containing significant genes are in red color, blocks containing mild significant genes are in light red color and blocks containing no significant genes are in green color.